PRECISION AG 2. 0 Conference February 11 -12, 2014 — Calgary, Alberta, Canada Precision Agriculture's Bold New Era: A Brief History, Current Expression and Radical New Directions …this presentation investigates the legacy of Precision Ag’s unique expression of Geotechnology, its current challenges, and its probable future directions Plenary address by Joseph K. Berry W. M. Keck Visiting Scholar in Geosciences, Department of Geography, University of Denver Adjunct Faculty, Warner College of Natural Resources, Colorado State University Principal, Berry & Associates // Spatial Information Systems Email jberry@innovativegis. com — Website www. innovativegis. com/basis/ (See http: //www. innovativegis. com/basis/present/PAconf_Calgary 2014/ to access support materials including Power. Point)
Geotechnology as a Mega-Technology Geotechnology is one of the three "mega -technologies" for the 21 st century and promises to forever change how we conceptualize, utilize and visualize spatial relationships in scientific research and commercial applications (U. S. Department of Labor) Geographic Information Systems (map and analyze) Remote Sensing Global Positioning System (location and navigation) (measure and classify) GPS/GIS/RS The Spatial Triad Computer Mapping (70 s) Spatial Database Management (80 s) Technological Tool Mapping involves precise placement (delineation) of physical features (graphical inventory) Map Analysis (90 s) Multimedia Mapping (00 s) is Where What Analytical Tool Modeling involves Descriptive Mapping Prescriptive Modeling Why So What and What If analysis of spatial patterns and relationships (map analysis/modeling) (Berry)
Historical Setting and Evolution of Precision Agriculture is a “site-specific management” technology that measures and responds to the spatial and temporal intra-field variations for maximizing crop production while preserving resources. PA’s development is closely aligned with Geotechnology (RS, GIS, GPS) and Robotics (intelligent implements). Computer Mapping (70 s Where) “Automated Image Analysis” Spectral Signature The Large Area Crop Inventory Experiment Healthy Sick Dead (LACIE) was the first large scale remote sensing project in agriculture demonstrating that improved accuracy in predictions of wheat production can be achieved by the use of satellite imagery. LACIE experimenters used Image analysis techniques to predict with great accuracy the size of the 1977 Soviet wheat crop six weeks prior to harvest. …foundational research in Machine Processing of Remotely Sensed Data “PA in Waiting” Spatial Database Management (80 s Where is What) The raging debate in GIS at the time was Discrete Spatial Objects (Vector) vs. Continuous Map Surfaces (Raster). Since the vector perspective more closely matched manual map-making and applications, it dominated GIS. But vector maps are of little use in farming and GPS was too inaccurate/ unreliable, so Precision Ag was stymied. Map Algebra that identified a set of grid-based primitive operations in a GIS allowing two or more geo-registered raster layers to produce a new raster layer using algebraic operations such as addition, subtraction etc. By sequencing these primitives operations, a Cartographic Modeling process analogous to solving equations is developed– it’s just that the variables are entire map layers composed of thousands of numbers. …technological advances improving Spatial/Temporal Resolution in RS and GPS (Berry)
Historical Setting and Evolution of Precision Agriculture is a “site-specific management” technology that measures and responds to the spatial and temporal intra-field variations for maximizing crop production while maintaining good land stewardship. PA’s development is closely aligned with Geotechnology (RS, GIS, GPS) and Robotics (intelligent implements). Map Analysis (90 s Why and So What) “Farm Maps as Data” Map Algebra concepts were extended by a more P rigorous mathematical/statistical framework (Map Analysis and GIS Modeling) and additional grid-based analytical capabilities were developed. K The raging debate in early Precision Ag circles was Management Zones (Discrete/Aggregated/Vector) N versus On-the-Fly (Continuous/Disaggregated/Raster). As GPS, satellite/aerial imaging and image processing became more precise, reliable and available, continuous data and processing approaches won out. With sub-meter positioning, hyperspectral detail and advanced grid analysis tools, Precision Ag moved from a research and innovation dominated field to a promising megaindustry. Networks of high-end workstations and desktop computers replaced old mainframe computers. …the pieces begin to fall into place and Adolescent PA Grows Up “All Systems Go” Geo. Web and Mobile Devices (00 s Wow!!!) The modern computing environment has radically changed from “stay-at-home” computers to powerful portable devices with high speed connectivity (e. g. , mobile phones, pads, tablets and notebooks). Cloud storage/computing provides access to vast amounts of GIS and RS data and processing. …like a perfect storm, the Precise Alignment of RS, GIS, GPS, Analytics and Robotics continues to fuel PA innovation and adoption (Berry)
GIS Development Cycle (…where we’ve been) GIS Evolution …Precision Ag’s expression of Geotechnology involves radically different technologies with extremely high expectations Geo. Web (2000 s) The lion’s share of growth has been GIS’s ever expanding capabilities as a “Technical Tool ” Contemporary GIS “It’s the only way to go, Frank. Why my life has changed, Ever since I discovered Stackable Livestock. …corralling vast amounts of spatial data and providing near instantaneous access to remote sensing images, GPS navigation, interactive maps, records management, geo-queries and awesome displays. Spatial d. B Mgt (1980 s) Map Analysis (1990 s) …about every decade The Early Years Mapping focus Data/Structure focus Analysis focus Computer Mapping (1970 s) But keep in mind— …PA is about doing the right thing at the right place and at the right time …it identifies and responds to variability within a field …it augments indigenous knowledge (not a replacement) —that are expressions of GIS as an “Analytical Tool” (Berry)
Some Examples of Soaring PA Technology Applications 1) Li. DAR Imaging vs. RTK GPS (terrain surface) Li. DAR for regional/state-wide surveys RTK GPS for farm-level survey Li. DAR and RTK for multistage terrain analysis RTK GPS Li. DAR Tom Buman’s Precision Conservation blog at http: //precisionconservation. com/ 2) Automated 3 D Machines (controlling positioning/hydraulics) Laser Leveling Field Grading to level a field Optimal Field Tile placement Variable-rate Seeding (depressions) Eyeball Leveling http: //www. fao. org/docrep/t 0231 e 08. htm/ 3) Remote Sensing Imagery and Drone Technology Remote Sensing: Satellite and Hyperspectral Imaging for crop development Drones: http: //www. specterra. com. au/precision_agriculture. html Geometric registration for Farm/Compliance Mapping Spectral analysis for Field Scouting Possibly for Spot Spraying (Future) http: //aerialfarmer. blogspot. com/ (Berry)
More Examples of Soaring PA Technology Applications 4) Ground Instrumentation for weather, soil moisture, harvesting Farm-based Weather Stations for disease, insect and water monitoring In-field network of Soil Moisture Probes for Evapotranspiration (ET) modeling Robotic Machines that can operate autonomously Advances in Crop Yield Monitor accuracy Advances in Field Sampling and Map Surface Generation Yield Monitor Mass Flow = time for material to move from the harvest point to the yield monitor GPS “Trolling for Yield Data” Physical Distance (1 sec at 4 mph = 5. 86 ft) Interpolation Error 5) New Technology Environment Faster/Cheaper/Smaller computers/tablets/phones Ubiquitous Connectivity from farm base to field to cafe Cloud Computing capabilities (more available and accessible) 6) Evolving Legal, Regulatory, Business and Social Environments As Applied Mapping for regulatory compliance and organic/GMO certification Data Ownership, Convertibility and Sharing will become increasingly important Integrated Platform Solutions from a few large companies will replace the disparate pieces of a solution from various small companies Scale, Expense and Cyber-phobia will continue as entry constraints but diminish as the farm community becomes more comfortable with computer technology (Berry)
Yield Limiting Factors (the basis of PA) üWater üWeather üTopography üNutrients üWeeds üPests üGenetics üSeeding Rate üOther… Candidate factor for Precision Agriculture and Site-specific Management if and only if — ü the factor is a significant driving variable ü it has measurable spatial variability ü its spatial variation can be explained and spatial relationships established ü it exhibits a spatial response to practical management actions …and results in production gains, increased profitability and/or improved stewardship (Berry)
Whole Field vs. Site Specific Management Aggregated Space Whole-field assumes the “average” conditions are the same everywhere within the field (uniform/homogenous) Management action is the same throughout the field Aggregated Space Discrete Management Zones Z 2 The bulk of agricultural research has been “non-spatial” (Spatially Aggregated) …but PA is all about disaggregated spatial relationships/patterns— break the field into areas of similar conditions (zones) Z 1 Management action is the same within each zone Z 1 Z 3 Z 2 …manage as a set of small irregular sub-fields Disaggregated Space Research Opportunity Continuous Map Surfaces break the field into small consistent pieces (grid cells) that track specific conditions at each grid location Management action varies continuously throughout the field (Berry)
Data Analysis Perspectives (Data Space vs. Geographic Space) Traditional Analysis Map Analysis (Data Space — Non-spatial Statistics) (Geographic Space — Spatial Statistics) Standard Normal Curve fit to the data (density function) Histogram Field Data Interpolated Surface fit to the data (density function) Point Data Plot Central Tendency Typical How Typical Average = 22. 0 St. Dev = 18. 7 28. 2 Continuous Spatial Distribution (Generalized) (Detailed) “Single Value” 22. 0 Discrete Spatial Object “Thousands of Values” Identifies the Typical Value Maps the Variance (Berry)
Grid-based Map Analysis Approaches Map Analysis involves three broad types of “Analytical Tools”— Interpolated Surface (Phosphorous Layer) Surface Modeling maps the spatial distribution of point-sampled data Ø Map Generalization— characterizes spatial trends (e. g. , tilted plane) Ø Spatial Interpolation— continuous spatial distribution (e. g. , IDW, Krig) Ø Other— roving windows and facets (e. g. , density surface, tessellation) Point Samples (P, K, N) Spatial Statistics investigates the “numerical” relationships in mapped data Ø Descriptive— aggregate statistics (e. g. , average, stdev, similarity, clustering) Ø Predictive— relationships among map layers (e. g. , regression) Ø Prescription— appropriate actions (e. g. , decision rules, optimization) Geo-registered Map Layers (P, K, N) Data Clusters (P, K, N) Spatial Analysis investigates the “contextual” relationships in mapped data Ø Reclassify— reassigns map values (e. g. , position, value, shape, contiguity) Ø Overlay— map layer coincidence (e. g. , point-by-point, region-wide, map-wide) Ø Distance— proximity and connection (e. g. , movement, optimal paths, visibility) Ø Neighbors— roving windows (e. g. , slope, aspect, diversity, anomaly) Erosion Potential Field Elevation fn(Slope, Flow) (Berry)
Geographic Distribution (Mapping the Variance) The “iterative smoothing” process is similar to slapping a chunk of modeler’s clay over the “data spikes, ” then taking a knife and cutting away the excess (successive smoothing) to leave a continuous surface that encapsulates the peaks and valleys implied in the field samples Numeric Distribution — Average, Standard Deviation Continuous Surface — Geographic Distribution (Berry)
Spatial Interpolation (soil nutrient levels) Spatial Interpolation maps the geographic distribution inherent in data sets Corn Field Phosphorous (P) Data “Spikes” IDW Surface (Berry)
Comparing Spatial Interpolation Results Comparison of the IDW interpolated surface to the whole field Average shows large differences in localized estimates (-16. 6 to 80. 4 ppm) Comparison of the IDW interpolated surface to the Krig interpolated surface shows small differences in localized estimates (-13. 3 to 11. 7 ppm) (Berry)
Grid-based Map Analysis Approaches Map Analysis involves three broad types of “Analytical Tools”— Interpolated Surface (Phosphorous Layer) Surface Modeling maps the spatial distribution of point-sampled data Ø Map Generalization— characterizes spatial trends (e. g. , tilted plane) Ø Spatial Interpolation— derives a continuous spatial distribution (e. g. , IDW, Krig) Ø Other— roving windows and facets (e. g. , density surface, tessellation) Point Samples (P, K, N) Spatial Statistics investigates the “numerical” relationships in mapped data Ø Descriptive— aggregate statistics (e. g. , average, stdev, similarity, clustering) Ø Predictive— relationships among map layers (e. g. , regression) Ø Prescription— appropriate actions (e. g. , decision rules, optimization) Geo-registered Map Layers (P, K, N) Data Clusters (P, K, N) Spatial Analysis investigates the “contextual” relationships in mapped data Ø Reclassify— reassigns map values (e. g. , position, value, shape, contiguity) Ø Overlay— map layer coincidence (e. g. , point-by-point, region-wide, map-wide) Ø Distance— proximity and connection (e. g. , movement, optimal paths, visibility) Ø Neighbors— roving windows (e. g. , slope, aspect, diversity, anomaly) Erosion Potential Field Elevation fn(Slope, Flow) (Berry)
Visualizing Spatial Relationships Interpolated Spatial Distribution Phosphorous (P) What spatial relationships do you see? …do relatively high levels of P often occur with high levels of K and N? …how often? …where? Humans can only “see” broad Generalized Patterns in a single map variable… (Berry)
Clustering Maps for Data Zones …but computers can “see” detailed patterns in multiple map variables (using Data Space) Geographic Space …groups of “floating balls” in data space identify locations in the field with similar data patterns– Data Zones (Data Clusters) …or a Continuous Equation precisely identifying the right action for each grid cell (Berry)
The Precision Ag Process (Fertility example) …there are four fundamental steps in the Precision Ag Process— Dependent Map Variable Prescription Map “Intelligent Implements” Yield Map On-the-Fly Zone 3 Nutrient Maps Derived Zone 2 Variable Rate Application Zone 1 Independent Map Variables 1) Data Collection 2) Data Analysis 3) Modeling 4) Management Action …the process is more generally termed Spatial Data Mining and is used in a host of applications from Geo-business to Epidemiology to Infrastructure Routing to Wildfire Risk Modeling …etc. and is analogous to non-spatial “Quantitative Data Analysis”— but uses “Map Variables” (Berry)
So Where Are We in Precision Ag? Yield Mapping …done deal for many crops Soil Nutrient Mapping …procedures need validation Mgt Zone Mapping …alternative approaches need study & validation Map Analysis and Modeling The Full Precision Farming Process …a fair piece to go IF <condition> THEN <action> …based on spatial relationship “rules” – Description (Where is What) …coming on line (Mapping) – Prediction (Why and So What) …needs lots of work (Inference) PA Research Nugget (Science) – Prescription (Do What Where) …barely on the research radar (Optimization) – Action (Precisely Here) …done deal for many farm inputs (location aware Robotics) (Berry)
Grid-based Map Analysis Approaches Map Analysis involves three broad types of “Analytical Tools”— Interpolated Surface (Phosphorous Layer) Surface Modeling maps the spatial distribution of point-sampled data Ø Map Generalization— characterizes spatial trends (e. g. , tilted plane) Ø Spatial Interpolation— derives a continuous spatial distribution (e. g. , IDW, Krig) Ø Other— roving windows and facets (e. g. , density surface, tessellation) Point Samples (P, K, N) Spatial Statistics investigates the “numerical” relationships in mapped data Ø Descriptive— aggregate statistics (e. g. , average, stdev, similarity, clustering) Ø Predictive— relationships among map layers (e. g. , regression) Ø Prescription— appropriate actions (e. g. , decision rules, optimization) Geo-registered Map Layers (P, K, N) Data Clusters (P, K, N) Spatial Analysis investigates the “contextual” relationships in mapped data Ø Reclassify— reassigns map values (e. g. , position, value, shape, contiguity) Ø Overlay— map layer coincidence (e. g. , point-by-point, region-wide, map-wide) Ø Distance— proximity and connection (e. g. , movement, optimal paths, visibility) Ø Neighbors— roving windows (e. g. , slope, aspect, diversity, anomaly) Erosion Potential Field Elevation fn(Slope, Flow) (Berry)
Micro Terrain Analysis (a simple field erosion/pooling model) Determining Erosion/Pooling. Potential: Slope classes (1= Gentle, 2=Moderate, 3= Steep) and Flow classes (1= Light, 2=Moderate, 3= Heavy Flows) …are combined into a single map identifying erosion/pooling potential 33= Steep & Heavy 1= Gentle 2= Mod 3= Steep 11= Gentle & Light times 10 plus 1= Light 2= Mod 3= Heavy renumber Field Elevation is formed by assigning an elevation value to each cell in an analysis grid (1 cm Lidar) …map of the Effective Movement (surface flow) of water, fine particles and organic matter within a field (Berry)
Precision Conservation (compared to Precision Ag) Precision Conservation Precision Ag Wind Erosion Chemicals Soil Erosion …closely related disciplines Runoff Leaching Terrain 2 -Dimensional Data Layers Soils 3 -Dimensional Movements Yield 2 D Square Potassium 3 D Cube Coincidence CIR Image Landscape Perspective Field Perspective (Ecological emphasis) (Production Emphasis) Landscape Field Precision Conservation connects farm fields, grasslands, rangelands and managed forests with their natural (Berry) surrounding areas such as buffers, riparian zones, natural forest, and water bodies. . . then uses information about localized surface and subsurface flows and cycles to analyze and better understand ecosystem processes leading to the best management practices for conservation and sustainability of agricultural, rangeland, and natural areas.
Deriving Erosion Potential (regional scale) Maps of surface Flow confluence and Slope steepness are calculated by considering relative elevation differences throughout a project area (Berry)
Calculating Effective Distance (variable-width buffers) Effective erosion buffers around a stream expand contract depending on the erosion potential of the intervening terrain Reach out farther under high Erosion Potential (Berry)
Water Conservation Modeling (Conservation = “wise use”) US Drought Monitor West Water Rights City Alternative Water Budget Crop Water Allocation Historic Crop Water Allocation Crops Purchase all rights Farm Income “Buy and Dry” Temporary Monitored Transfers City Water “Win-Win” To River Farmland Grid-based Map Analysis/Modeling of consumptive water needs and optimization to increase farm revenue based on New/Expanded Instrumentation: Water Flow Measurements — Evapotranspiration Monitoring — Soil Moisture Measurements — Remote Sensing http: //www. regenmg. com/Home. aspx Sustainable Water and Innovative Irrigation Management (SWIIM) (Berry)
GIS Development Cycle (…where we’re heading) Future Directions GIS Evolution Revisit Analytics Future Directions 2 D Planar 3 D Solid (X, Y Data) (2020 s) (X, Y, Z Data) Cartesian Coordinates Geo. Web (2000 s) Revisit Geo-reference (2010 s) Square (4 sides) Cube (6 squares) Hexagon (6 sides) Pentagonal Dodecahedral (12 pentagons) Contemporary GIS Spatial d. B Mgt (1980 s) Map Analysis (1990 s) …about every decade The Early Years Mapping focus Data/Structure focus Analysis focus Computer Mapping (1970 s) (Berry)
Where To Go From Here… www. innovativegis. com/basis/ Online References Breakout Session Returning the Scientific Horse to in Front of the Technical Cart …a math/stat framework for Map Analysis Analyzing Precision Ag Data …downloadable book with hands-on exercises Beyond Mapping Compilation Series …Beyond Mapping columns appearing in Geo. World magazine from March 1989 through December 2013 Organized into four downloadable books Presentation handout (Berry)
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